SoroushVahidi/scidocs huggingface dataset:
Making Smart Contracts Smarter
(2016)

Cryptocurrencies record transactions in a decentralized data structure called a blockchain. Two of the most popular cryptocurrencies, Bitcoin and Ethereum, support the feature to encode rules or scripts for processing transactions. This feature has evolved to give practical shape to the ideas of smart contracts, or full-fledged programs that are run on blockchains. Recently, Ethereum's smart contract system has seen steady adoption, supporting tens of thousands of contracts, holding millions dollars worth of virtual coins.
 In this paper, we investigate the security of running smart contracts based on Ethereum in an open distributed network like those of cryptocurrencies. We introduce several new security problems in which an adversary can manipulate smart contract execution to gain profit. These bugs suggest subtle gaps in the understanding of the distributed semantics of the underlying platform. As a refinement, we propose ways to enhance the operational semantics of Ethereum to make contracts less vulnerable. For developers writing contracts for the existing Ethereum system, we build a symbolic execution tool called Oyente to find potential security bugs. Among 19, 336 existing Ethereum contracts, Oyente flags 8, 833 of them as vulnerable, including the TheDAO bug which led to a 60 million US dollar loss in June 2016. We also discuss the severity of other attacks for several case studies which have source code available and confirm the attacks (which target only our accounts) in the main Ethereum network.
Enumerating the Non-Isomorphic Assembly Configurations of Modular Robotic Systems
(1998)

A \modular" robotic system consists of joint and link modules that can be assembled in a variety of conngurations to meet diierent or changing task requirements. However, due to typical symmetries in module design, diierent assembly conngurations may lead to robotic structures which are kinematically identical, or isomorphic. This paper considers how to enumerate the non-isomorphic assembly conngurations of a modular robotic system. We introduce an Assembly Incidence Matrix (AIM) to represent a modular robot assembly con-guration. Then we use symmetries of the module geometry and graph isomorphisms to deene an equivalence relation on the AIMs. Equivalent AIMs represent isomorphic robot assembly conngurations. Based on this equivalence relation, we propose an algorithm to generate non-isomorphic assembly conngurations of an n-link tree-like robot with diierent joint and link module types. Examples demonstrate that this method is a signiicant improvement over a brute force enumeration process.
Locally Normalized Filter Banks Applied to Deep Neural-Network-Based Robust Speech Recognition
(2017)

This letter describes modifications to locally normalized filter banks (LNFB), which substantially improve their performance on the Aurora-4 robust speech recognition task using a Deep Neural Network-Hidden Markov Model (DNN-HMM)-based speech recognition system. The modified coefficients, referred to as LNFB features, are a filter-bank version of locally normalized cepstral coefficients (LNCC), which have been described previously. The ability of the LNFB features is enhanced through the use of newly proposed dynamic versions of them, which are developed using an approach that differs somewhat from the traditional development of delta and delta–delta features. Further enhancements are obtained through the use of mean normalization and mean–variance normalization, which is evaluated both on a per-speaker and a per-utterance basis. The best performing feature combination (typically LNFB combined with LNFB delta and delta–delta features and mean–variance normalization) provides an average relative reduction in word error rate of 11.4% and 9.4%, respectively, compared to comparable features derived from Mel filter banks when clean and multinoise training are used for the Aurora-4 evaluation. The results presented here suggest that the proposed technique is more robust to channel mismatches between training and testing data than MFCC-derived features and is more effective in dealing with channel diversity.
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
(2017)

Probabilistic forecasting, i.e. estimating the probability distribution of a time series’ future given its past, is a key enabler for optimizing business processes. In retail businesses, for example, forecasting demand is crucial for having the right inventory available at the right time at the right place. In this paper we propose DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an auto-regressive recurrent network model on a large number of related time series. We demonstrate how by applying deep learning techniques to forecasting, one can overcome many of the challenges faced by widely-used classical approaches to the problem. We show through extensive empirical evaluation on several real-world forecasting data sets that our methodology produces more accurate forecasts than other state-of-the-art methods, while requiring minimal manual work.
IMPACTS DUE TO INTERNET ADDICTION AMONG MALAYSIAN UNIVERSITY STUDENTS Azizah Zainudin
(2013)

The purpose of this study is to study the impacts due to Internet addiction among Malaysian university students. Research methodology used in this study was by distributing survey questions to 653 university students from five different universities in Malaysia. There were four possible impacts measured in this research study which include Academic Performances, Relationships, Personality and Lifestyle. The finding shows that, Internet addiction cause problems with respondents’ academic performances, having bad personality and practicing an unhealthy lifestyle. There were significantly differences in academic performances, personality and lifestyle between “Average user” and “Excessive user”. The above matter will be further discussed throughout this
The Role of Asymmetric Dimethylarginine (ADMA) in Endothelial Dysfunction and Cardiovascular Disease
(2010)

Endothelium plays a crucial role in the maintenance of vascular tone and structure. Endothelial dysfunction is known to precede overt coronary artery disease. A number of cardiovascular risk factors, as well as metabolic diseases and systemic or local inflammation cause endothelial dysfunction. Nitric oxide (NO) is one of the major endothelium derived vaso-active substances whose role is of prime importance in maintaining endothelial homeostasis. Low levels of NO are associated with impaired endothelial function. Asymmetric dimethylarginine (ADMA), an analogue of L-arginine, is a naturally occurring product of metabolism found in human circulation. Elevated levels of ADMA inhibit NO synthesis and therefore impair endothelial function and thus promote atherosclerosis. ADMA levels are increased in people with hypercholesterolemia, atherosclerosis, hypertension, chronic heart failure, diabetes mellitus and chronic renal failure. A number of studies have reported ADMA as a novel risk marker of cardiovascular disease. Increased levels of ADMA have been shown to be the strongest risk predictor, beyond traditional risk factors, of cardiovascular events and all-cause and cardiovascular mortality in people with coronary artery disease. Interventions such as treatment with L-arginine have been shown to improve endothelium-mediated vasodilatation in people with high ADMA levels. However the clinical utility of modifying circulating ADMA levels remains uncertain.
Sindice.com: a document-oriented lookup index for open linked data
(2008)

Data discovery on the SemanticWeb requires crawling and indexing of statements, in addition to the ‘linked-data’ approach of de-referencing resourceURIs. Existing SemanticWeb search engines are focused on database-like functionality, compromising on index size, query performance and live updates.Wepresent Sindice, a lookup indexover SemanticWeb resources. Our index allows applications to automatically locate documents containing information about a given resource. In addition, we allow resource retrieval through inverse-functional properties, offer a full-text search and index SPARQL endpoints. Finally, we extend the sitemap protocol to efficiently index large datasets with minimal impact on data providers.
Evaluating radial, axial and transverse flux topologies for 'in-wheel' motor
(2004)

Three different motor topologies, (1) radial, (2) axial and (3) transverse flux, are investigated for 'in-wheel' electric propulsion system. The design constraints are: 350mm/spl times/100mm package limitation, 48 V DC bus restriction and per phase inverter current limited to 500 A/sup pk/. In addition, magnet pole pairs are fixed to 9 due to limitation in 'position sensor' capacity. The paper summarizes results obtained from analytical design followed by 2D and 3D static and transient finite element analysis accompanied with experimental verification for the radial flux topology.
Removing Shadows from Images
(2002)

We attempt to recover a 2D chromaticity intrinsic variation of a single RGB image which is independent of lighting, without requiring any prior knowledge about the camera. The proposed algorithm aims to learn an invariant direction for projecting from 2D color space to the greyscale intrinsic image. We notice that along this direction, the entropy in the derived invariant image is minimized. The experiments conducted on various inputs indicate that this method achieves intrinsic 2D chromaticity images which are free of shadows. In addition, we examined the idea to utilize projection pursuit instead of entropy minimization to find the desired direction.
Kwyjibo: automatic domain name generation
(2008)

Automatically generating ‘good’ domain names that are random yet pronounceable is a problem harder than it first appears. The problem is related to random word generation, and we survey and categorize existing techniques before presenting our own syllable-based algorithm that produces higher-quality results. Our results are also applicable elsewhere, in areas such as password generation, username generation, and even computer-generated poetry. Copyright © 2008 John Wiley & Sons, Ltd.
Multi-session PLDA scoring of i-vector for partially open-set speaker detection
(2013)

This paper advocates the use of probabilistic linear discriminant analysis (PLDA) for partially open-set detection task with multiple i-vectors enrollment condition. Also referred to as speaker verification, the speaker detection task has always been considered under an open-set scenario. In this paper, a more general partially open-set speaker detection problem in considered, where the imposters might be one of the known speakers previously enrolled to the system. We show how this could be coped with by modifying the definition of the alternative hypothesis in the PLDA scoring function. We also look into the impact of the conditionalindependent assumption as it was used to derive the PLDA scoring function with multiple training i-vectors. Experiments were conducted using the NIST 2012 Speaker Recognition Evaluation (SRE’12) datasets to validate various points discussed in the paper.
The effects of prosocial video games on prosocial behaviors: international evidence from correlational, longitudinal, and experimental studies.
(2009)

Although dozens of studies have documented a relationship between violent video games and aggressive behaviors, very little attention has been paid to potential effects of prosocial games. Theoretically, games in which game characters help and support each other in nonviolent ways should increase both short-term and long-term prosocial behaviors. We report three studies conducted in three countries with three age groups to test this hypothesis. In the correlational study, Singaporean middle-school students who played more prosocial games behaved more prosocially. In the two longitudinal samples of Japanese children and adolescents, prosocial game play predicted later increases in prosocial behavior. In the experimental study, U.S. undergraduates randomly assigned to play prosocial games behaved more prosocially toward another student. These similar results across different methodologies, ages, and cultures provide robust evidence of a prosocial game content effect, and they provide support for the General Learning Model.
Oral squamous cell carcinoma overview.
(2009)

Most cancer in the head and neck is squamous cell carcinoma (HNSCC) and the majority is oral squamous cell carcinoma (OSCC). This is an overview of oral squamous cell carcinoma (OSCC) highlighting essential points from the contributors to this issue, to set the scene. It emphasises the crucial importance of prevention and the necessarily multidisciplinary approach to the diagnosis and management of patients with OSCC.
Hybrid crafting: towards an integrated practice of crafting with physical and digital components
(2013)

With current digital technologies, people have large archives of digital media, such as images and audio files, but there are only limited means to include these media in creative practices of crafting and making. Nevertheless, studies have shown that crafting with digital media often makes these media more cherished and that people enjoy being creative with their digital media. This paper aims to open up the way for novel means for crafting, which include digital media in integrations with physical construction, here called ‘hybrid crafting’. Notions of hybrid crafting were explored to inform the design of products or systems that may support these new crafting practices. We designed ‘Materialise’—a building set that allows for the inclusion of digital images and audio files in physical constructions by using tangible building blocks that can display images or play audio files, alongside a variety of other physical components—and used this set in four hands-on creative workshops to gain insight into how people go about doing hybrid crafting; whether hybrid crafting is desirable; what the characteristics of hybrid crafting are; and how we may design to support these practices. By reflecting on the findings from these workshops, we provide concrete guidelines for the design of novel hybrid crafting products or systems that address craft context, process and result. We aim to open up the design space to designing for hybrid crafting because these new practices provide interesting new challenges and opportunities for future crafting that can lead to novel forms of creative expression.
MOA: Massive Online Analysis
(2010)

MassiveOnline Analysis (MOA) is a software environment for implementing al orithms and running experiments for online learning from evolving data str eams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Na ı̈ve Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license.
Deep Learning with Limited Numerical Precision
(2015)

Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of low-precision fixed-point computations, we observe the rounding scheme to play a crucial role in determining the network’s behavior during training. Our results show that deep networks can be trained using only 16-bit wide fixed-point number representation when using stochastic rounding, and incur little to no degradation in the classification accuracy. We also demonstrate an energy-efficient hardware accelerator that implements low-precision fixed-point arithmetic with stochastic rounding.
Learning Deep Features for Scene Recognition using Places Database
(2014)

[1] L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Image Understanding, 2007. [2] G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. 2007. [3] S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proc. CVPR, 2006. [4] L.-J. Li and L. Fei-Fei. What, where and who? classifying events by scene and object recognition. In Proc. ICCV, 2007. [5] G. Patterson and J. Hays. Sun attribute database: Discovering, annotating, and recognizing scene attributes. In Proc. CVPR, 2012.
A 240 G-ops/s Mobile Coprocessor for Deep Neural Networks
(2014)

Deep networks are state-of-the-art models used for understanding the content of images, videos, audio and raw input data. Current computing systems are not able to run deep network models in real-time with low power consumption. In this paper we present nn-X: a scalable, low-power coprocessor for enabling real-time execution of deep neural networks. nn-X is implemented on programmable logic devices and comprises an array of configurable processing elements called collections. These collections perform the most common operations in deep networks: convolution, subsampling and non-linear functions. The nn-X system includes 4 high-speed direct memory access interfaces to DDR3 memory and two ARM Cortex-A9 processors. Each port is capable of a sustained throughput of 950 MB/s in full duplex. nn-X is able to achieve a peak performance of 227 G-ops/s, a measured performance in deep learning applications of up to 200 G-ops/s while consuming less than 4 watts of power. This translates to a performance per power improvement of 10 to 100 times that of conventional mobile and desktop processors.
Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks
(2015)

Convolutional neural network (CNN) has been widely employed for image recognition because it can achieve high accuracy by emulating behavior of optic nerves in living creatures. Recently, rapid growth of modern applications based on deep learning algorithms has further improved research and implementations. Especially, various accelerators for deep CNN have been proposed based on FPGA platform because it has advantages of high performance, reconfigurability, and fast development round, etc. Although current FPGA accelerators have demonstrated better performance over generic processors, the accelerator design space has not been well exploited. One critical problem is that the computation throughput may not well match the memory bandwidth provided an FPGA platform. Consequently, existing approaches cannot achieve best performance due to under-utilization of either logic resource or memory bandwidth. At the same time, the increasing complexity and scalability of deep learning applications aggravate this problem. In order to overcome this problem, we propose an analytical design scheme using the roofline model. For any solution of a CNN design, we quantitatively analyze its computing throughput and required memory bandwidth using various optimization techniques, such as loop tiling and transformation. Then, with the help of rooine model, we can identify the solution with best performance and lowest FPGA resource requirement. As a case study, we implement a CNN accelerator on a VC707 FPGA board and compare it to previous approaches. Our implementation achieves a peak performance of 61.62 GFLOPS under 100MHz working frequency, which outperform previous approaches significantly.
Topology Derivation of Nonisolated Three-Port DC–DC Converters From DIC and DOC
(2013)

A systematic approach is proposed for the derivation of nonisolated three-port converter (TPC) topologies based on dual-input converters (DIC) and dual-output converters (DOC), which serves as an interface for a renewable source, a storage battery, and a load simultaneously. The power flow in a TPC is analyzed and compared with that in a DIC or a DOC, respectively. Beginning with building the power flow paths of a TPC from a DIC or a DOC, the general principles and detailed procedures for the generation and optimization of TPC topologies are presented. Based on these works, a family of nonisolated TPC topologies is developed. The derived TPCs feature single-stage power conversion between any two of the three ports, and result in high integration and high efficiency. One of the proposed TPCs, named Boost-TPC, is taken as an example for verifying the performance of its circuit topology with related operational methods. Pulsewidth modulation and power management methods, used in this circuit, are analyzed in detail. Experiments have been carried out on a 1-kW prototype of the Boost-TPC, which demonstrate the feasibility and effectiveness of the proposed topology derivation method.
A framework for cognitive Internet of Things based on blockchain
(2018)

Internet of Things, cognitive systems, and blockchain technology are three fields which have created numerous revolutions in software development. It seems that a combination among these fields may results in emerging a high potential and interesting field. Therefore, in this paper, we propose a framework for Internet of Things based on cognitive systems and blockchain technology. To the best of our knowledge, there is no framework for Internet of Things based on cognitive systems and blockchain. In order to study the applicability of the proposed framework, a recommender system based on the proposed framework is suggested. Since the proposed framework is novel, the suggested recommender system is novel. The suggested recommender system is compared with the existing recommender systems. The results show that the suggested recommender system has several benefits which are not available in the existing recommender systems.
Management patterns: SDN-enabled network resilience management
(2014)

Software-defined networking provides abstractions and a flexible architecture for the easy configuration of network devices, based on the decoupling of the data and control planes. This separation has the potential to considerably simplify the implementation of resilience functionality (e.g., traffic classification, anomaly detection, traffic shaping) in future networks. Although software-defined networking in general, and OpenFlow as its primary realisation, provide such abstractions, support is still needed for orchestrating a collection of OpenFlow-enabled services that must cooperate to implement network-wide resilience. In this paper, we describe a resilience management framework that can be readily applied to this problem. An important part of the framework are policy-controlled management patterns that describe how to orchestrate individual resilience services, implemented as OpenFlow applications.